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IEEE International Conference on data engineering

作者： Yu Sun Rui Zhang Andy Yuan Xue Jianzhong Qi Xiaoyong Du Department of Computing and Information Systems University of Melbourne Renmin University of China and Key Laboratory of Data Engineering and Knowledge Engineering MOE

ISBN: (纸本)9781509020218

We study the problem of constructing a reverse nearest neighbor (RNN) heat map by finding the RNN set of every point in a two-dimensional space. Based on the RNN set of a point, we obtain a quantitative influence (i.e., heat) for the point. The heat map provides a global view on the influence distribution in the space, and hence supports exploratory analyses in many applications such as marketing and resource management. To construct such a heat map, we first reduce it to a problem called Region Coloring (RC), which divides the space into disjoint regions within which all the points have the same RNN set. We then propose a novel algorithm named CREST that efficiently solves the RC problem by labeling each region with the heat value of its containing points. In CREST, we propose innovative techniques to avoid processing expensive RNN queries and greatly reduce the number of region labeling operations. We perform detailed analyses on the complexity of CREST and lower bounds of the RC problem, and prove that CREST is asymptotically optimal in the worst case. Extensive experiments with both real and synthetic data sets demonstrate that CREST outperforms alternative algorithms by several orders of magnitude.

关键词： Heating Radio frequency Indexes

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Search Result Diversification Based on Query Facets

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Journal of Computer Science & Technology 2015年第4期30卷 888-901页

作者：胡莎窦志成王晓捷文继荣 School of Information Renmin University of China Beijing 100872 China Key Laboratory of Data Engineering and Knowledge Engineering Ministry of Education Beijing 100872 China

In search engines, different users may search for different information by issuing the same query. To satisfy more users with limited search results, search result diversification re-ranks the results to cover as many user intents as possible. Most existing intent-aware diversification algorithms recognize user intents as subtopics, each of which is usually a word, a phrase, or a piece of description. In this paper, we leverage query facets to understand user intents in diversification, where each facet contains a group of words or phrases that explain an underlying intent of a query. We generate subtopics based on query facets and propose faceted diversification approaches. Experimental results on the public TREC 2009 dataset show that our faceted approaches outperform state-of-the-art diversification models.

关键词： query intent query facet search result diversification

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State of the art in knowledge extraction from online polls: A survey of current technologies 16

State of the art in knowledge extraction from online polls: ...

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Australasian Computer Science Week Multiconference, ACSW 2016

作者： Stabauer, Martin Grossmann, Georg Stumptner, Markus Department of Data Processing in Social Sciences Economics and Business Johannes Kepler University Linz Austria Knowledge and Software Engineering Laboratory School of Information Technology and Mathematical Sciences University of South Australia Australia Advanced Computing Research Centre School of Information Technology and Mathematical Sciences University of South Australia Australia

ISBN: (纸本)9781450340427

The ongoing research and development in the field of Natural Language Processing has lead to a great number of technologies in its context. There have been major benefits when it comes to bringing together the worlds of natural language and semantic technologies, so more and more potential areas of application emerge. One of these is the subject of this paper, in particular the possible ways of knowledge extraction from single-question online polls. With concepts of the Social Web, internet users want to contribute and express their opinion. As a consequence, the popularity of online polls is rapidly increasing;they can be found in news articles of media sites, on blogs etc. It would be desirable to bring intelligence to the application of polls by using technologies of the Semantic Web and Natural Language Processing as this would allow to build a great knowledge base and to draw conclusions from it. This paper surveys the current landscape of tools and state-of-the-art technologies and analyses them with regard to pre-defined requirements that need to be accomplished, in order to be useful for extracting knowledge from the results generated by online polls. © 2016 ACM.

关键词： Surveys

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Formalizing UML Model Metrics Using Z Language

Formalizing UML Model Metrics Using Z Language

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作者： Fangjun Wu School of Information Technology Jiangxi University of Finance and Economics Jiangxi Key Laboratory of Data and Knowledge Engineering Jiangxi University of Finance and Economics

Till now, a large variety of researchers have carried out lots of efforts on object-oriented and UML model metrics from different views. They put forward numerous of metrics and carried out some series of theoretical and experimental verifications on understandability, analyzability, maintainability, fault-proneness, change-proneness and reuse. However, there is no formal semantic specification for UML model metrics, which may lead to potential semantic inconsistency and ambiguity. To solve this problem, this paper provided formalization for UML model metrics at the level of UML Meta models. This formalization can not only help people to understand the meaning of UML model metrics, but also can be used in the application domain of UML model metrics in a more rigorous way.

关键词： software measurement object-oriented UML class diagrams Z language empirical validation theoretical verification

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Random analysis of statistical rough set

Random analysis of statistical rough set

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International Conference on Machine Learning and Cybernetics (ICMLC)

作者： Eric C. C. Tsang Su-Yun Zhao Faculty of Information Technology Macau University of Science and Technology Macau China MOE Key Laboratory of Data Engineering and Knowledge Engineering (Renmin University of China) Beijing China

ISBN: (纸本)9781509003914

Attribute reduction is an inevitable problem in machine learning and statistical learning. To improve the traditional rough set reduction, statistical rough sets is then proposed by introducing random sampling into the rough approximation. Random sampling is the main contribution of statistical rough sets. As a result, it is necessary to analyze the randomness of statistical rough sets. In this paper, we analyze and demonstrate the influence of the randomness in the process of attribute reduction by a large number of experiments to test the effectiveness and stability of the random sampling.

关键词： Rough sets Standards Cybernetics Fluctuations Stability analysis Big data Algorithm design and analysis

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Exploratory subgroup analytics on ubiquitous data 4

Exploratory subgroup analytics on ubiquitous data

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4th International Workshop on Mining Ubiquitous and Social Environments, MUSE 2013 in conjunction with the European Conference on Machine Learning and Principles and Practice of knowledge Discovery in databases, ECML-PKDD 2013

作者： Atzmueller, Martin Mueller, Juergen Becker, Martin Knowledge and Data Engineering Group University of Kassel Kassel Germany Data Mining and Information Retrieval Group University of Würzburg Würzburg Germany

ISBN: (纸本)9783319147222

This paper presents exploratory subgroup analytics on ubiquitous data: We propose subgroup discovery and assessment approaches for obtaining interesting descriptive patterns and provide a novel graphbased analysis approach for assessing the relations between the obtained subgroup set. This exploratory visualization approaches allows for the comparison of subgroups according to their relations to other subgroups and to include further parameters, e.g., geo-spatial distribution indicators. We present and discuss analysis results utilizing real-world data given by geo-tagged noise measurements with associated subjective perceptions and a set of tags describing the semantic context. ©Springer International Publishing Switzerland 2015

关键词： Semantics

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Mining opinion word from customer review

International Journal of Database Theory and Application

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International Journal of database Theory and Application 2016年第2期9卷 129-136页

作者： Tengjiao, Jiang Minjuan, Zhong Shumei, Liao Siwen, Luo School of Information Technology Jiangxi University of Finance and Economics Nanchang China Jiangxi Key Laboratory of Data and Knowledge Engineering Jiangxi University of Finance and Economics Nanchang China

Online customer review is considered as a significant informative resource which is useful for both potential customer and product manufacturers. As a result, it is one of the most challenging tasks to mine customer reviews automatically and to provide users with opinion summary. Product features and opinion word play the most important roles in the customers' opinions mining. In this paper, we dedicate our work to opinion word mining. We proposed an approach for opinion word identification based on the association rule mining algorithm. The method makes full use of co-occurrence syntactic characteristic between product features and opinion word. Firstly, the product feature is identified by two-stage filtering scheme, and secondly the opinion word is extracted through association rule mining. The final experiment results show that the proposed method could not only obtain the product features related to domain characteristics, but identify the opinion word effectively. Meanwhile, our approach possesses much higher precision and recall than Hu's work.

关键词： Association rules

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RDF partitioning for scalable SPARQL query processing

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Frontiers of Computer Science 2015年第6期9卷 919-933页

作者： Xiaoyan WANG Tao YANG Jinchuan CHEN Long HE Xiaoyong DU School of Information Renmin University of China Beijing 100872 China Key Laboratory of Data Engineering and Knowledge Engineering of Ministry of Education Renmin University Beijing 100872 China Information Center Supreme People's Court Beijing 100745 China State Key Laboratory of Software Development Environment Beihang University Beijing 100191 China

The volume of RDF data increases dramatically within recent years, while cloud computing platforms like Hadoop are supposed to be a good choice for processing queries over huge data sets for their wonderful scalability. Previous work on evaluating SPARQL queries with Hadoop mainly focus on reducing the number of joins through careful split of HDFS files and algorithms for generating Map/Reduce jobs. However, the way of partitioning RDF data could also affect system performance. Specifically, a good partitioning solution would greatly reduce or even to- tally avoid cross-node joins, and significantly cut down the cost in query evaluation. Based on HadoopDB, this work processes SPARQL queries in a hybrid architecture, where Map/Reduce takes charge of the computing tasks, and RDF query engines like RDF-3X store the data and execute join operations. According to the analysis of query workloads, this work proposes a novel algorithm for automatically parti- tioning RDF data and an approximate solution to physically place the partitions in order to reduce data redundancy. It also discusses how to make a good trade-off between query evaluation efficiency and data redundancy. All of these pro- posed approaches have been evaluated by extensive experiments over large RDF data sets.

关键词： RDF data data partitioning SPARQL query

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Load pattern window aware power supply device clustering

International Journal of Database Theory and Application

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International Journal of database Theory and Application 2016年第8期9卷 269-280页

作者： Sheng, Wanxing Liu, Ke-Yan Yu, Yixi An, Rungong Zhou, Ningnan Zhang, Xiao China Electric Power Research Institute Beijing100192 China Key Laboratory of Data Engineering and Knowledge Engineering Ministry of Education Renmin University of China Beijing100872 China School of Information Renmin University of China 100872 China

data-driven decision in big data era is becoming ubiquitous in electronic grid. In particular, daily collected power consumption records enable workload aware device clustering, which is crucial for critical domain applications such as device functionality identification. In this paper, we propose a load pattern window aware method for clustering power supply devices. Our approach overcomes the drawbacks in existing works, such as fuzzy based clustering, K-means based clustering and neutral network based clustering. After investigating the large scale records from power supply devices, our approach partitions device records into disjoint time intervals with parameterized window size, which indicate the load pattern feature for a period of time given a specific device. Devices are then decomposed into a mixture of these features, and those devices with similar dominating features are grouped together. The experimental results demonstrate the effectiveness and efficiency of our solution based on the real data collected from power grid in China. © 2016 SERSC.

关键词： Big data

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Publish me and protect me: Personalized and flexible location privacy protection in mobile social networks 23

Publish me and protect me: Personalized and flexible locatio...

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23rd IEEE International Symposium on Quality of Service, IWQoS 2015

作者： Wu, Yao Peng, Hui Zhang, Xiaoying Chen, Hong Li, Cuiping Key Laboratory of Data Engineering and Knowledge Engineering of Ministry of Education Beijing China School of Information Renmin University of China Beijing China

ISBN: (纸本)9781467371131

With the increasing proliferation of the Mobile Social Networks (MSN) and the Location Based Service (LBS), location privacy has attracted broad attention in recent years. Most researches have been done with the assumption that the server is untrusted and a trusted third party is introduced to protect the user location privacy when a user sends queries for the location service. In this paper, we reconsider this assumption and propose a Personalized and Flexible location privacy protection Model (PFM) based on user relationship strength. We conduct researches in the situation that the server is trusted while malicious users in the MSN can disguise as a friend to break location privacy. We present an entropic TF-IDF based approach to measure the bi-directional relationship strength and propose probability distribution based cloaking model to protect user location privacy. We thoroughly evaluate our methods based on Quality of Privacy (QoP) via real and synthetic data. © 2015 IEEE.

关键词： Location based services

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